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11institutetext: David R. Cheriton School of Computer Science, University of Waterloo, Canada 11email: biedl@uwaterloo.ca 22institutetext: Department of Computer Science, University of Saskatchewan, Canada 22email: d.mondal@usask.ca

Improved Outerplanarity Bounds for Planar Graphs

Therese Biedl Corresponding author.
A preliminary version of the paper appeared at the 50th International Workshop on Graph-Theoretic Concepts in Computer Science (WG 2024) [5].11
   Debajyoti Mondal 22
Abstract

In this paper, we study the outerplanarity of planar graphs, i.e., the number of times that we must (in a planar embedding that we can initially freely choose) remove the outerface vertices until the graph is empty. It is well-known that there are nn-vertex graphs with outerplanarity n6+Θ(1)\tfrac{n}{6}+\Theta(1), and not difficult to show that the outerplanarity can never be bigger. We give here improved bounds of the form n2g+2g+O(1)\tfrac{n}{2g}+2g+O(1), where gg is the fence-girth, i.e., the length of the shortest cycle with vertices on both sides. This parameter gg is at least the connectivity of the graph, and often bigger; for example, our results imply that planar bipartite graphs have outerplanarity n8+O(1)\tfrac{n}{8}+O(1). We also show that the outerplanarity of a planar graph GG is at most 12diam(G)+O(n)\tfrac{1}{2}\text{\rm diam}(G)+O(\sqrt{n}), where diam(G)\text{\rm diam}(G) is the diameter of the graph. All our bounds are tight up to smaller-order terms, and a planar embedding that achieves the outerplanarity bound can be found in linear time.

Keywords:
Planar graphs Outerplanarity Fence girth Diameter.

inline,color=red!70!white]Discuss (TB\rightarrowDM): Some renaming of variables should still happen to be consistent.
* gg should perhaps be greek (γ\gamma? or something that captures fence, maybe ϕ\phi J: φ\varphi looks nicer, ϕ\phi reminds me of empty set - should I replace?). T: At this point I have gotten so used to gg that φ\varphi (or perhaps ψ\psi) might confuse me. Let me mull this over longer.
* We’re fairly inconsistent whether to write g/2g/2, 12g\tfrac{1}{2}g or g2\tfrac{g}{2}. My suggestion would be to switch everything to g2\tfrac{g}{2} when the numerator/denominator have no sub/superscripts and to 12g\tfrac{1}{2}g^{*} otherwise. I’m changing this as I’m finding them.

inline,color=red!70!white]Discuss (TB\rightarrowDM): Some English questions.
* outerplanarity or outer-planarity?
* outerface or outer-face (or even outer face)? All of the above are correct in some style-guides, so I don’t really care but we should be consistent. Without dash or space is shortest.

1 Introduction

The outerplanarity of a planar graph is a well-known tool, both for deriving efficient algorithms and for proving lower bounds for graph drawings. It measures how often we have to remove the vertices on the outerface (a peel) until the graph is empty. (Detailed definitions are in Section 2.) In this paper, we obtain better upper bounds on the outerplanarity of a planar graph, which is important from the perspective of the following two application areas.

The first application of outerplanarity is to design faster algorithms for various problems in planar graphs. Baker [3] showed that for a planar graph with constant outerplanarity, numerous graph problems, such as independent set, vertex cover, dominating set can all be solved in linear time. (There are numerous generalizations, see e.g. [16, 17, 12, 20, 21].) The running times of many such algorithms have an exponential dependency on the outerplanarity or related parameters. Hence an upper bound on the outerplanarity with respect to the size of the graph can provide an estimate of how large of a graph these algorithms may be able to process in practice.

Another major application of outerplanarity is to derive lower bounds for various optimization criteria in graph drawing. For example, there exists a planar graph with a fixed planar embedding (known as nested triangles graph) that requires at least a 23n×23n\tfrac{2}{3}n\times\tfrac{2}{3}n-grid in any of its straight-line grid drawings that respect the given embedding (attributed to Leiserson [24] by Dolev, Trickey and Leighton [15]). Here a grid drawing maps each vertex to a grid point and each edge to a straight line segment between its end vertices. The crucial ingredient to their proof is that the nested triangles graph has n3\tfrac{n}{3} peels (in this embedding), and any embedding-preserving planar straight-line grid-drawing of a planar graph with kk peels requires at least a 2k×2k2k\times 2k-grid. (In fact, this lower bound holds for many other planar graph drawing styles [1, 19, 31].) Nested triangles graphs have outerplanarity n6\tfrac{n}{6}, and thus gives a lower bound of an n3×n3\tfrac{n}{3}\times\tfrac{n}{3}-grid for the planar straight-line grid drawing even when one can freely choose an embedding to draw the graph. This raises a natural question of whether n6\tfrac{n}{6} is the largest outerplanarity (perhaps up to lower-order terms) that a planar graph can have. This turns out to be true, via a detour into the radius, which we discuss next.

The eccentricity of a vertex vv in GG is the smallest integer kk such that the shortest-path distance from vv to any other vertex in GG is at most kk. The radius of GG (denoted rad(G)\text{rad}(G)) is the smallest eccentricity over all the vertices of GG, while the diameter of GG (denoted diam(G)\text{diam}(G)) is the largest eccentricity. For 3-connected planar graphs, Harant [22] proved an upper bound of rad(G)n6+Δ+32\text{\rm rad}(G)\leq\frac{n}{6}+\Delta^{*}+\frac{3}{2}, where Δ\Delta^{*} is the maximum degree of the dual graph, i.e., the maximum length of a face. Ali et al. [2] improved the upper bound to n6+5Δ6+56\frac{n}{6}+\frac{5\Delta^{*}}{6}+\frac{5}{6} and more generally n2κ+O(Δ)\frac{n}{2\kappa}+O(\Delta^{*}), where κ\kappa is the connectivity of the graph; these bounds are tight within an additive constant. This easily implies upper bounds on the outerplanarity.

Observation 1.1 ()

Every planar graph GG has outerplanarity at most min{1+rad(G),n+266}\min\{1+\text{\rm rad}(G),\tfrac{n+26}{6}\}, and this bound holds even if the spherical embedding of GG is fixed.

Proof

We first prove the radius-bound. Use as outerface a face that is incident to a vertex vv of eccentricity rad(G)\text{\rm rad}(G). Then all vertices zz with dG(v,z)=i1d_{G}(v,z)=i-1 belong to the iith peel or an earlier one, so after removing rad(G)+1\text{\rm rad}(G)+1 peels the graph is empty.

For the second bound, arbitrarily add edges to GG to make it into a maximal planar graph G+G^{+}. This is triangulated, so using the result by Ali et al. [2] we have rad(G+)n+206\text{\rm rad}(G^{+})\leq\tfrac{n+20}{6} and hence outerplanarity at most n+266\tfrac{n+26}{6}. The outerplanarity of subgraph GG cannot be bigger. ∎

A triangulated graph GG is a maximal planar graph; in any planar embedding, faces then have length 3. It is folklore that for a triangulated graph, the difference between radius and outerplanarity is at most 1. But for graphs with greater face-lengths, the two parameters become very different (consider a cycle). The radius-bound on 3-connected graphs by Ali et al. increases as the faces get bigger, while one would expect the outerplanarity to decrease as faces get bigger. So our goal in this paper is to find bounds on the outerplanarity that do not depend on the face-lengths and improve on n6\tfrac{n}{6} for some graphs. We use a parameter that we call the fence-girth: In a planar graph GG with a fixed embedding, a fence is a cycle CC with other vertices both strictly inside and strictly outside CC, and the fence-girth is the shortest length of a fence. (For a graph without cycles, the fence-girth is \infty.) Our main result is the following:

  1. C1. Every planar graph has outerplanarity at most n22g+O(g)\lfloor\tfrac{n-2}{2g}\rfloor+O(g) for any integer g3g\geq 3 that is at most the fence-girth. We can find a planar embedding with this number of peels in linear time. Some graphs with fence-girth gg have outerplanarity at least n22g\lfloor\tfrac{n-2}{2g}\rfloor. (Section 4).

We are not aware of prior results for outerplanarity-bounds, but since the radius is closely related to it for triangulated graphs, we contrast our result to the best radius-bound of n2κ+O(Δ)\tfrac{n}{2\kappa}+O(\Delta^{*}) by Ali et al. [2]. The fence-girth is never less than the connectivity κ\kappa, so our theorem implies outerplanarity n2κ+O(1)\tfrac{n}{2\kappa}+O(1), where κ5\kappa\leq 5. Hence up to small constant terms our bound is never worse than Ali et al.’s, and often it will be better. For example, for bipartite planar graphs the fence-girth is at least 4, so with g=4g=4 we obtain a bound of n8+O(1)\tfrac{n}{8}+O(1), whereas Ali et al.’s bound is only n6+O(1)\tfrac{n}{6}+O(1). Secondly, the prior bound held only for 3-connected planar graphs, while we make no such restrictions. Finally, we can find a suitable embedding in linear time while all existing algorithms for outerplanarity [6, 23] take quadratic time or more.

For a triangulated graph GG, the fence-girth is the same as the connectivity κ5\kappa\leq 5. Result C1. hence implies that rad(G)n2κ+O(1)\text{\rm rad}(G)\leq\tfrac{n}{2\kappa}+O(1). This bound was previously known [2], but our result comes with a linear-time algorithm to find a vertex with this eccentricity, which is new:

  1. C2. For a κ\kappa-connected triangulated graph GG, we can find a vertex ss with dG(s,z)n22κ+O(1)d_{G}(s,z)\leq\lfloor\tfrac{n-2}{2\kappa}\rfloor+O(1) for all zV(G)z\in V(G) in linear time (Section 4).

Ali et al. did not study the run-time to find a vertex of small eccentricity; while their proof could be turned into an algorithm, its run-time would be O(nrad(G))O(n\cdot\text{\rm rad}(G)), hence quadratic. The known subquadratic algorithms for computing the radius of a planar graph are far from being linear [8, 18, 30], and algorithms that provide (1+ϵ)(1+\epsilon)-approximation have running time of the form O(f(1/ϵ)nlog2n)O(f(1/\epsilon)n\log^{2}n) [9, 29], where ff is a polynomial function on (1/ϵ)(1/\epsilon). Linear-time algorithms for the radius are only known for special subclasses of planar graph classes [11, 16].

Since the outerplanarity (for triangulated graphs) is closely related to the radius, and the radius is closely related to the diameter, it is natural to ask to bound the outerplanarity in terms of the diameter. We can show the following:

  1. C3. Every planar graph GG has outerplanarity at most 12diam(G)+O(n)\tfrac{1}{2}\text{\rm diam}(G)+O(\sqrt{n}), and a corresponding embedding can be found in linear time. Every triangulated graph GG has radius 12diam(G)+O(n)\tfrac{1}{2}\text{\rm diam}(G)+O(\sqrt{n}), and a vertex of this eccentricity can be found in linear time. (Section 5).

Similar results with a ‘correction term’ of O(n)O(\sqrt{n}) have been studied before, for example, Boitmanis et al. [7] gave an algorithm that computes the diameter and radius within such an error term in O(|E(G)|n)O(|E(G)|\sqrt{n}) time. So our contribution is that we can find a vertex of eccentricity 12diam(G)+O(n)\tfrac{1}{2}\text{\rm diam}(G)+O(\sqrt{n}) in linear time, hence faster than Boitmanis et al. [7].

We also show that this bound is tight and that the correction-term O(n)O(\sqrt{n}) cannot be avoided, not even for triangulated graphs. In particular, this answers (negatively) a question on MathOverflow [28] whether rad(G)12diam(G)+O(1)\text{\rm rad}(G)\leq\tfrac{1}{2}\text{\rm diam}(G)+O(1) for all triangulated graphs; such a relationship does hold for interval graphs [26], chordal graphs [27], and various grid graphs and generalizations [11].

  1. C4. There exists a triangulated graph GG with radius 12diam(G)+Ω(n)\tfrac{1}{2}\text{\rm diam}(G)+\Omega(\sqrt{n}) (Section 5).

2 Definitions

We assume familiarity with graph theory and planar graphs (see for example [14]) and fix throughout a planar graph GG with nn vertices. For a path π\pi in GG, the length |π||\pi| is its number of edges. For two vertices y,zy,z, write dG(y,z)d_{G}(y,z) for the length of the shortest path between them; we only need undirected graph distance, i.e., if GG has directed edges then this measures the distance in the underlying undirected graph. For a set of vertices LL, write GLG\setminus L for the graph obtained by deleting the vertices in LL and G[L]:=G(VL)G[L]:=G\setminus(V\setminus L) for the graph induced by LL. We need the following separator theorem for trees:

Theorem 2.1

[25] Let 𝒯{\cal T} be a tree with non-negative node-weights w()w(\cdot). Then in linear time we can find a node SS such that for every subtree 𝒯{\cal T}^{\prime} of 𝒯S{\cal T}\setminus S we have w(𝒯)12w(𝒯)w({\cal T}^{\prime})\leq\tfrac{1}{2}w({\cal T}), where w(𝒯)w({\cal T}^{\prime}) denotes the sum of weights of nodes in 𝒯{\cal T}^{\prime}.

One easily derived consequence of the separator theorem is the following:

Observation 2.2 ()

Any connected graph GG has a vertex with eccentricity at most n2\lfloor\tfrac{n}{2}\rfloor that we can find in linear time.

Proof

Fix a spanning tree 𝒯{\cal T} of the graph, and let ss be the separator-node from Theorem 2.1, using unit weights. Then any subtree 𝒯{\cal T}^{\prime} of 𝒯{s}{\cal T}\setminus\{s\} contains at most n/2\lfloor n/2\rfloor nodes, and so the distance from ss to any other node is at most n/2\lfloor n/2\rfloor. ∎

A spherical embedding of GG describes a drawing Γ\Gamma of GG on a sphere Σ\Sigma by listing for each face (maximal region of ΣΓ\Sigma\setminus\Gamma) the closed walk(s) of GG that bound the face. Graph GG is called triangulated if all faces are triangles; the spherical embedding is then unique. A planar embedding of GG is a drawing of GG in the plane described by giving a spherical embedding Γ\Gamma and fixing one face FF (the outerface) which becomes the infinite face in the planar drawing.

For the following definition, assume that GG is plane (comes with a fixed planar embedding (Γ,F)(\Gamma,F)). Define the peels [23] of GG as follows: L1L_{1} consists of all vertices on the outerface FF. For i>1i>1, LiL_{i} consists of all vertices on the outerface of G(L1Li1)G\setminus(L_{1}{\cup}\dots\cup L_{i-1}), where this graph uses as planar embedding the one inherited from GG. The number of peels (which depends on Γ\Gamma and FF) is the minimum number kk such that G(L1Lk)G\setminus(L_{1}{\cup}\dots\cup L_{k}) is the empty graph. We use the term fixed-spherical-embedding (fse) outerplanarity of GG for the minimum number of peels over all choices of outerface FF (but keeping the same spherical embedding Γ\Gamma). The (unrestricted) outerplanarity of GG is the minimum number of peels over all choices of spherical embedding Γ\Gamma and outerface FF of Γ\Gamma.

3 Toolbox

In this section, we give some definitions and methods that will be used by multiple proofs later. Throughout, we assume that the input graph GG comes with a fixed spherical embedding which we will never change. We also assume that GG is connected, for if it is not then we can add edges between components that share a face until GG is connected. This does not add cycles (so does not change the fence-girth) and it can only decrease the diameter (hence improve the outerplanarity bound), therefore adding such edges does not affect our results.

The tree of peels:

We will compute a tree 𝒯{\cal T} that stores, roughly speaking, the hierarchy of peels for some outerface, see also Figure 1. Formally, pick a root-vertex rr arbitrarily, except that it should not be a cutvertex. Choose as outerface of GG a face incident to rr. Define L0:={r}L_{0}:=\{r\} and compute the peels L1,,LkL_{1},\dots,L_{k} of GL0G\setminus L_{0}. These layers L0,L1,,LkL_{0},L_{1},\dots,L_{k} are not quite the peels of GG (because we start with one vertex rather than a face), and not quite the layers of a breadth-first search (BFS) tree (because we include in the next layer all vertices that share a face with vertices of the previous layer, whether they are adjacent or not). We direct each edge (y,z)(y,z) from the higher-indexed to the lower-indexed layer; edges connecting vertices within a layer remain undirected.

We organize the layers L0,,LkL_{0},\dots,L_{k} into a tree 𝒯{\cal T} (the tree of peels) as follows: The root of 𝒯{\cal T} is a node RR that corresponds to the entire graph GG; define V(R):={r}V(R):=\{r\}. For i=1,2,3,i=1,2,3,\dots, add a node NKN_{K} to 𝒯{\cal T} for each connected component KK of G(L0Li1)G\setminus(L_{0}{\cup}\dots\cup L_{i-1}). Component KK is part of one connected component PP of G(L0Li2)G\setminus(L_{0}{\cup}\dots\cup L_{i-2}); make NKN_{K} a child of the node NPN_{P} corresponding to PP in 𝒯{\cal T}. Define V(NK)V(N_{K}) to be the outerface vertices of KK.

Refer to caption
(a)  
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(b)  
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(c)  
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(d)  
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(e)  
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(f)  
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(g)  
Figure 1: (a) A plane graph GG. (b)–(e) The graphs obtained by deleting L0,L1,,L3L_{0},L_{1},\ldots,L_{3}, respectively. Solid vertices are the set V(N)V(N) of the corresponding node NN. (f) The tree of peels 𝒯\cal T. (g) The augmentation HH.

Throughout this paper, we will use ‘node’ (and upper-case letters) for the elements of 𝒯{\cal T} while we reserve ‘vertex’ (and lower-case letters) for GG. An interior node of 𝒯{\cal T} is a node that is neither the root nor a leaf. We think of each node NN as ‘storing’ the vertices in V(N)V(N) and observe that these vertices induce a connected subgraph. Also, every vertex of GG is stored at exactly one node of 𝒯{\cal T}. We need a few easy observations:

Observation 3.1 ()

The following holds for the tree of peels 𝒯{\cal T}:

  1. 1.

    The root RR of 𝒯{\cal T} has a single child.

  2. 2.

    Let Y,ZY,Z be the nodes that store the ends y,zy,z of an edge ee. Then either ee is undirected and Y=ZY{=}Z, or ee is directed (say yzy\rightarrow z) and ZZ is the parent of YY.

  3. 3.

    For any interior node NN of 𝒯{\cal T}, the size |V(N)||V(N)| is at least the fence-girth.

Proof

(1) We chose root-vertex rr so that it is not a cutvertex; therefore GL0=GrG\setminus L_{0}=G\setminus r is connected and there is only one node that is a child of RR.

(2) For edge (y,z)(y,z), let ii be the smallest index for which layer LiL_{i} contains yy or zz. If both y,zy,z are in LiL_{i}, then y,zy,z belong to the same node since they are in one connected component. If one of them (say yy) is not in LiL_{i}, then yLi+1y\in L_{i+1}, so the edge is directed yzy\rightarrow z and YY becomes a child of ZZ since the edge (y,z)(y,z) ensures that yy is in the connected component that defined ZZ.

(3) Recall that node NN corresponds to a connected component KNK_{N} of the graph obtained by deleting some of the levels. Since NN is not a leaf, subgraph KNK_{N} has at least one vertex vv not on the outerface. Therefore, the outerface of KNK_{N} contains a cycle that has vv inside and rr outside (since NRN\neq R). This cycle is a fence and all its vertices belong to V(N)V(N). ∎

Augmenting GG:

It will be helpful if every vertex except root-vertex rr has an outgoing edge. In general, this need not hold for our input graph GG. We therefore augment GG with further edges. The following result was shown in [4]; the result there was for the peels while our definition of layers L0,,LkL_{0},\dots,L_{k} is slightly different, but one easily verifies that the proof carries over.

Claim 3.2

(based on Obs. 2 in [4]) We can add edges to GG (while maintaining planarity) such that for all i1i\geq 1 every vertex in LiL_{i} has a neighbour in Li1L_{i-1}.

Let HH be the graph obtained by adding a set of directed edges such every vertex except rr has an outgoing edge in HH (Figure 1(g)). Because we only add edges between adjacent layers, the following is easily shown.

Observation 3.3 ()

The augmented graph HH has the same tree of peels as GG (assuming we start with the inherited planar embedding and outerface and use the same root-vertex).

Proof

Observe first that since we start with the same root-vertex and outerface, and only add directed edges, color=blue!50!white]FYI: We used to assume that HH is minimal, but I dont want to waste effort on discussing how to find this efficiently. So downgraded this to ‘add only directed edges’, which is easy to achieve. the layers L0,,LkL_{0},\dots,L_{k} are exactly the same in both GG and HH. Assume HH is obtained by adding just one edge e=(y,z)e=(y,z) (the full proof is then by induction on the number of added edges). Observe that y,zy,z belong to one face FF of GG since we can add edge (y,z)(y,z) to GG while staying planar. Since GG is connected, so is the boundary of FF. For any face the vertices belong to at most two consecutive layers by definition of peels; for the specific face FF the vertices belong to exactly two consecutive layers (say LiL_{i} and Li+1L_{i+1}) since they include y,zy,z which are not on the same layer by construction of HH. So there exists in GG a path π\pi (along the boundary of FF) that connects yy and zz and has all vertices in LiL_{i} and Li+1L_{i+1}.

With this, the connected components that define the tree of peels are exactly the same for both graphs. To see this, observe that when we have removed L0Lh1L_{0}\cup\dots\cup L_{h-1} for some hih\leq i, then edge (y,z)(y,z) provides no connectivity among components that we did not have via path π\pi instead. Once we have removed LiL_{i}, one of y,zy,z (and hence the added edge) has been removed from the graph, and so again does not add any connectivity. ∎

Refer to caption
Figure 2: Larger example of 𝒯\cal T and the detour-method to connect s0A0s_{0}\in A_{0} with z0A0z_{0}\in A_{0}, as well as ss to zz as in Claim 4.2. Not all directed edges are shown for clarity.

Adding edges to GG may decrease the fence-girth, but not the node-sizes of 𝒯{\cal T}, which is all that will be used below. We will in the following only consider graph HH, so every vertex zrz\neq r has an outgoing edge.

The detour-method:

We need the following method to connect two given vertices of HH that are stored in the same node of 𝒯{\cal T} (Figure 3(a) and Figure 2).

Definition 1

Fix a node A0A_{0} of 𝒯{\cal T}, and two vertices s0,z0V(A0)s_{0},z_{0}\in V(A_{0}), as well as exit conditions ξ0,ξ1,ξ2,\xi_{0},\xi_{1},\xi_{2},\dots which are (possibly negative) integers that will be specified by each application. The detour method at node A0A_{0} finds a path connecting s0s_{0} and z0z_{0} as follows:

  • Initialize i=0i=0; we have si,ziV(Ai)s_{i},z_{i}\in V(A_{i}). We will also maintain paths τs\tau_{s} (from s0s_{0} to sis_{i}) and τz\tau_{z} (from z0z_{0} to ziz_{i}); initially these are simply s0\langle s_{0}\rangle and z0\langle z_{0}\rangle.

  • If dH(si,zi)ξid_{H}(s_{i},z_{i})\leq\xi_{i}, then set σ\sigma to be a path from sis_{i} to ziz_{i} that has distance at most ξi\xi_{i}. Exit with ‘success’ and return ii and τs,τz,σ\tau_{s},\tau_{z},\sigma.

  • If dH(si,zi)>ξid_{H}(s_{i},z_{i})>\xi_{i}, and AiA_{i} was the root, then return ‘fail’.

  • Otherwise let Ai+1A_{i+1} be the parent of AiA_{i}. Find directed edges sisi+1s_{i}\rightarrow s_{i+1} and zizi+1z_{i}\rightarrow z_{i+1}, append them to paths τs\tau_{s} and τz\tau_{z}, update ii+1i\leftarrow i{+}1 and repeat.

Since each iteration gets us closer to the root, the algorithm must terminate. If it exits successfully, say at index ii^{*}, then we get a walk s0:; τssi:: 𝜎zi :; τzz0.s_{0}{\overset{\tau_{s}}{\>\text{{\char 58\relax\char 59\relax} }\!}}s_{i^{*}}{\overset{\sigma}{\>\text{{\char 58\relax\char 58\relax} }\!}}z_{i^{*}}{\overset{\tau_{z}}{\raisebox{4.30554pt}{\rotatebox{180.0}{$\,\>\text{{\char 58\relax\char 59\relax} }\!\,$}}}}z_{0}. To bound the length of this walk, the following observation will be useful:

Observation 3.4 ()

If the detour-method does not succeed at index i>0i>0, and AiA_{i} is not the root, then 12|V(Ai)|ξi+1\lfloor\tfrac{1}{2}|V(A_{i})|\rfloor\geq\xi_{i}{+}1.

color=blue!50!white]proof kicked to appendix

Proof

Recall that there are directed edges sj1sjs_{j-1}\rightarrow s_{j} and zj1zjz_{j-1}\rightarrow z_{j} by j>0j>0 and that sj1,zj1Aj1s_{j-1},z_{j-1}\in A_{j-1}. Write GjG_{j} for the graph induced by V(Aj)V(A_{j}), and observe that V(Aj1)V(A_{j-1}) must all belong to one inner face FF of GjG_{j} since it bounds a connected component of the subgraph of GG where the peels up to AjA_{j} have been removed. Furthermore, sj,zjs_{j},z_{j} have neighbours both strictly inside and strictly outside FF (due to their outgoing edges). So there must be a simple cycle CC along the boundary of FF that contains both sjs_{j} and zjz_{j}. Walking along the shorter side of CC hence gives a walk from sjs_{j} to zjz_{j} of length at most |C|/2|V(Aj)|/2\lfloor|C|/2\rfloor\leq\lfloor|V(A_{j})|/2\rfloor. By j<ij<i^{*} the stopping-condition did not hold at AjA_{j}, so this implies |V(Aj)/>ξi\lfloor|V(A_{j})/\rfloor>\xi_{i} and the result holds by integrality. ∎

We demonstrate how to use the detour-method with the following result that will be needed later. For any node NN, write 𝐚(N){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(N) for the number of vertices stored at strict ancestors of NN.

Lemma 1 ()

Assume that |V(N)|3|V(N)|\geq 3 for all interior nodes NN. For any node A0A_{0} and any s0,t0V(A0)s_{0},t_{0}\in V(A_{0}) we have dH(s0,t0)max{2𝐚(A0)2,4}d_{H}(s_{0},t_{0})\leq\max\{\allowbreak 2\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(A_{0})}\rceil{-}2,\allowbreak 4\}.

Proof

We are done if A0A_{0} is the root RR or its child or a grandchild of RR, for then we can connect s0s_{0} and t0t_{0} with a path of length at most 4 by following directed edges until we reach root-vertex rr. So assume that A0A_{0} has a parent PP, grand-parent G1G_{1} and great-grandparent G2G_{2}, therefore 𝐚(A0)|V(P)|+|V(G1)|+|V(G2)|3+3+1=7{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(A_{0})\geq|V(P)|+|V(G_{1})|+|V(G_{2})|\geq 3+3+1=7 since PP and G1G_{1} are internal nodes. Hence 𝐚(A0)3\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(A_{0})}\rceil\geq 3 and the desired upper bound is 2𝐚(A0)242\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(A_{0})}\rceil-2\geq 4. For ease of writing define β=𝐚(A0)30\beta=\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(A_{0})}\rceil-3\geq 0, so the desired upper bound becomes 2β+42\beta+4. Apply the detour-method at A0A_{0}, using ξi=2β+42i\xi_{i}=2\beta+4-2i for i0i\geq 0. If the method returns successfully at index ii^{*} with paths τs,τz,σ\tau_{s},\tau_{z},\sigma, then combining the three paths gives d(s0,t0)|τs|+|τz|+|σ|2i+2β+42id(s_{0},t_{0})\leq|\tau_{s}|+|\tau_{z}|+|\sigma|\leq 2i^{*}+2\beta+4-2i^{*} as desired.

So now assume for contradiction that the detour-method fails, and let Ai,si,ziA_{i},s_{i},z_{i} (for i=0,,i=0,\dots,\ell) be the nodes and vertices that it used; we have A=RA_{\ell}=R since the method can only fail at the root. Since the detour-method did not succeed at RR, color=blue!50!white]FYI: This needed a major rewrite; Obs.3.4 doesn’t hold for the root note. we did not have a path of length at most ξ\xi_{\ell} from ss_{\ell} to zz_{\ell}. But |R|=1|R|=1, so s=r=zs_{\ell}=r=z_{\ell} are connected by a path of length 0. So 0>ξ=2β+420>\xi_{\ell}=2\beta+4-2\ell or >β+2\ell>\beta+2. Now bound the sizes of V(A1),,V(A)V(A_{1}),\dots,V(A_{\ell}) as follows:

  • For i=1,,β+2i=1,\dots,\beta+2, Observation 3.4 implies |V(Ai)|2(ξi+1)=4β+104i>3β+84i|V(A_{i})|\geq 2(\xi_{i}+1)=4\beta+10-4i>3\beta+8-4i.

  • For i=β+1i=\beta{+}1, this bound becomes |V(Ai)|4β+104(β+1)=6|V(A_{i})|\geq 4\beta+10-4(\beta{+}1)=6.

  • For i=β+2i=\beta{+}2, we also know |V(Ai)|3|V(A_{i})|\geq 3 since AiA_{i} is not the root by >β+2\ell>\beta+2.

  • Root AA_{\ell} stores one vertex rr.

We therefore have a contradiction:

(β+3)2\displaystyle(\beta+3)^{2} 𝐚(A0)i=1|V(Ai)|i=1β(3β+84i)+6+3+1\displaystyle\geq{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(A_{0})\geq\sum_{i=1}^{\ell}|V(A_{i})|\geq\sum_{i=1}^{\beta}\big{(}3\beta{+}8{-}4i\big{)}+6+3+1
>i=1β(3β+6)+i=1β(24i)+9\displaystyle>\sum_{i=1}^{\beta}\big{(}3\beta{+}6\big{)}+\sum_{i=1}^{\beta}\big{(}2{-}4i\big{)}+9
=3β2+6β2i=1β(2i1)+9=3β2+6β2β2+9=(β+3)2\displaystyle=3\beta^{2}+6\beta-2\sum_{i=1}^{\beta}(2i{-}1)+9=3\beta^{2}+6\beta-2\beta^{2}+9=(\beta+3)^{2}

Refer to caption
(a)  
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(b)  
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(c)  
Refer to caption
(d)  
Figure 3: (a) Path-finding for nodes in V(𝒯R)V({\cal T}_{R}) when ZZ is not a descendent of SS. (b) Path-finding by using a detour at SS when ZZ is a descendent of SS. (c) Path-finding for deep descendants of SS. (d) Finding a small common ancestor of deep nodes. inline]The green is barely visible in a B/W printout.

4 Outerplanarity and fence-girth

In this section, we first prove that any planar graph GG has outerplanarity at most n22g+O(g)\lfloor\tfrac{n-2}{2g}\rfloor+O(g) where g3g\geq 3 is a (user-given) integer that is supposed to be at most the fence-girth.111We use a parameter gg that is separate from the fence-girth since the latter can be Θ(n)\Theta(n); to minimize the upper bound one should set gg to be min{fence-girth,12n2}\min\{\text{fence-girth},\tfrac{1}{2}\sqrt{n{-}2}\}. Then we discuss implications and lower bounds.

Separator-node SS:

To prove the upper bound, define the layers L0,L1,L_{0},L_{1},\dots, augmentation HH and tree of peels 𝒯{\cal T} as in Section 3. If any interior node of 𝒯{\cal T} stores fewer than gg vertices, then repoert that gg was too big (cf. Obs. 3.1(3)) and abort. Otherwise, apply the separator theorem (Theorem 2.1) to the tree 𝒯{\cal T}, using node-weight w(N):=|V(N)|w(N):=|V(N)|, i.e., the number of stored vertices. Let SS be a node such that any subtree 𝒯{\cal T}^{\prime} of 𝒯S{\cal T}\setminus S stores at most n2\tfrac{n}{2} vertices; we write V(𝒯)V({\cal T}^{\prime}) for these stored vertices. We know that SRS\neq R, because the root has only one child, and w(𝒯R)=n1>n2w({\cal T}\setminus R)=n-1>\tfrac{n}{2}.

Crucially, SS is close to all other nodes of 𝒯{\cal T}. Since we will frequently need the following upper bound, we introduce a convenient notation δ\delta for it.

Observation 4.1

For any node ZZ of 𝒯{\cal T} we have d𝒯(S,Z)δ:=n22g+1d_{\cal T}(S,Z)\leq\delta:=\lfloor\tfrac{n-2}{2g}\rfloor+1.

Proof

Let Π\Pi be the path from SS to ZZ in 𝒯{\cal T}. Every interior node NN of Π\Pi belongs to the same subtree 𝒯{\cal T}^{\prime} of 𝒯S{\cal T}\setminus S, and satisfies |V(N)|g|V(N)|\geq g since it is neither leaf nor root. Node ZSZ\neq S also belongs to 𝒯{\cal T}^{\prime} and |V(Z)|1|V(Z)|\geq 1. Altogether therefore n2|V(𝒯)|g(|Π|1)+1\tfrac{n}{2}\geq|V({\cal T}^{\prime})|\geq g(|\Pi|{-}1)+1 or |Π|n22g+1|\Pi|\leq\tfrac{n-2}{2g}+1, which implies d𝒯(S,Z)=|Π|δd_{{\cal T}}(S,Z)=|\Pi|\leq\delta by integrality. ∎

The overall idea of our proof is now to pick a vertex sV(S)s\in V(S), and to argue that dH(s,z)δ+2g2d_{H}(s,z)\leq\delta+2g-2 for all vertices zz. Actually, for most cases below, this will hold for any choice of sSs\in S.

Vertices stored in 𝒯R{\cal T}_{R}:

Let 𝒯R{\cal T}_{R} be the subtree of 𝒯S{\cal T}\setminus S that contains root RR. For vertices in this subtree, the detour method proves the distance-bound.

Claim 4.2

Assume that g3g\geq 3. For any sV(S)s\in V(S) and any zV(𝒯R)z\in V({\cal T}_{R}) we have dH(s,z)δ+gd_{H}(s,z)\leq\delta+g.

Proof

Figure2 and 3(a) illustrate this proof. Let ZZ be the node that stores zz, and let A0A_{0} be the least common ancestor of SS and ZZ; this is a strict ancestor of SS since Z𝒯RZ\in{\cal T}_{R}. Follow directed edges from ss to some vertex s0V(A0)s_{0}\in V(A_{0}); the resulting path πs\pi_{s} has length d𝒯(S,A0)d_{\cal T}(S,A_{0}) since every directed edge gets us closer to the root. Likewise we can get a path πz\pi_{z} of length d𝒯(Z,A0)d_{\cal T}(Z,A_{0}) from zz to some node z0V(A0)z_{0}\in V(A_{0}) (possibly z0=zz_{0}=z).

Now apply the detour-method with A0,s0,z0A_{0},s_{0},z_{0}, using ξi=g1\xi_{i}=g{-}1. This will always exit with ‘success’ at a node AiA_{i^{*}} that is not the root, because any two vertices stored at the child of the root have outgoing edges towards the root-vertex rr and hence distance 2g12\leq g{-}1. Let τs,τz,σ\tau_{s},\tau_{z},\sigma be the paths, then π:=s:; πss0:; τssi:: 𝜎zi :; τzz0 :; πzz\pi:=s{\overset{\pi_{s}}{\>\text{{\char 58\relax\char 59\relax} }\!}}s_{0}{\overset{\tau_{s}}{\>\text{{\char 58\relax\char 59\relax} }\!}}s_{i^{*}}{\overset{\sigma}{\>\text{{\char 58\relax\char 58\relax} }\!}}z_{i^{*}}{\overset{\tau_{z}}{\raisebox{4.30554pt}{\rotatebox{180.0}{$\,\>\text{{\char 58\relax\char 59\relax} }\!\,$}}}}z_{0}{\overset{\pi_{z}}{\raisebox{4.30554pt}{\rotatebox{180.0}{$\,\>\text{{\char 58\relax\char 59\relax} }\!\,$}}}}z has length d𝒯(S,Z)+2i+|σ|d_{{\cal T}}(S,Z)+2i^{*}+|\sigma|.

To bound |π||\pi|, consider the path Π\Pi from SS to ZZ in 𝒯{\cal T}, which goes through A0A_{0}, and let A1,,AiA_{1},\dots,A_{i^{*}} be the ancestors of A0A_{0} that were visited by the detour-method. If i>0i^{*}>0, then by Obs. 3.4 we have |V(Aj)|2(ξj+1)=2g|V(A_{j})|\geq 2(\xi_{j}{+}1)=2g for j=1,,i1j=1,\dots,i^{*}{-}1, node AiA_{i^{*}} and the interior nodes of Π\Pi store at least gg vertices each which nodes ZZ and RR store at least one vertex. Therefore

n2|V(𝒯R)|2g(i1)+g+(|Π|1)g+1+1g(|Π|+2i2)+2,\tfrac{n}{2}\geq|V({\cal T}_{R})|\geq 2g(i^{*}{-}1)+g+(|\Pi|{-}1)g+1+1\geq g(|\Pi|+2i^{*}-2)+2,

hence |Π|+2in42g+2|\Pi|+2i^{*}\leq\tfrac{n{-}4}{2g}+2 which is at most δ+1\delta{+}1 by integrality. If i=0i^{*}=0 then |Π|+2iδ|\Pi|+2i^{*}\leq\delta by Obs. 4.1. Either way |π|=|Π|+2i+|σ|δ+1+(g1)|\pi|=|\Pi|+2i^{*}+|\sigma|\leq\delta+1+(g{-}1). ∎

Vertices at descendants of SS:

In light of Claim 4.2, we only need to worry about vertices that are stored at descendants of SS. We first introduce two methods to find short paths for these in special situations. Recall that 𝐚(S){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S) denotes the number of vertices stored at strict ancestors of node SS.

Claim 4.3

Assume that 𝐚(S)g2{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)\leq g^{2} and g3g\geq 3. Then for any sV(S)s\in V(S) and any vertex zz stored at a descendant ZZ of SS we have dH(s,z)δ+2g2d_{H}(s,z)\leq\delta+2g-2.

Proof

See also Figure 3(b). Use directed edges to find a path πz\pi_{z} of length d𝒯(Z,S)δd_{\cal T}(Z,S)\leq\delta from zz to some vertex z0V(S)z_{0}\in V(S). We know dH(s,z0)max{2𝐚(S)2,4}2g2d_{H}(s,z_{0})\leq\max\{2\lfloor\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}\rfloor{-}2,4\}\leq 2g-2 by Lemma 1. Combining the paths gives the desired length: dH(z,s)dH(z,z0)+dH(z0,s)δ+2g2d_{H}(z,s)\leq d_{H}(z,z_{0})+d_{H}(z_{0},s)\leq\delta+2g-2. ∎

Claim 4.4

Let DD be a descendant of SS. Let sDV(D)s_{D}\in V(D) be a vertex of eccentricity at most |V(D)|2\lfloor\tfrac{|V(D)|}{2}\rfloor in the connected graph H[V(D)]H[V(D)] (Obs. 2.2). Follow directed edges from sDs_{D} to reach a vertex sV(S)s\in V(S). Then for any vertex zz stored at a descendant ZZ of DD we have dH(s,z)d𝒯(Z,S)+|V(D)|2d_{H}(s,z)\leq d_{\cal T}(Z,S)+\lfloor\tfrac{|V(D)|}{2}\rfloor.

Proof

See Figure 3(c). Use directed edges to go from zz to a vertex zDV(D)z_{D}\in V(D) along a path πz\pi_{z} of length d𝒯(Z,D)d_{\cal T}(Z,D). Find a path σ\sigma of length at most |V(D)|2\lfloor\tfrac{|V(D)|}{2}\rfloor to connect zDz_{D} to sDs_{D}, and then follow d𝒯(D,S)d_{\cal T}(D,S) directed edges to get to ss. Combining the paths gives the desired length since d𝒯(Z,D)+d𝒯(D,S)=d𝒯(Z,S)d_{\cal T}(Z,D)+d_{\cal T}(D,S)=d_{\cal T}(Z,S).∎

Corollary 1

Assume that |V(S)|4g3|V(S)|\leq 4g-3. Then there exists an sV(S)s\in V(S) such that for any vertex zz stored at a descendant of SS we have dH(s,z)δ+2g2d_{H}(s,z)\leq\delta+2g-2.

So we are done if 𝐚(S)g2{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)\leq g^{2} or |V(S)|4g3|V(S)|\leq 4g-3. Otherwise we distinguish the descendants of SS by their depth, using Claim 4.4 (for a carefully chosen DD) for the ‘deep’ ones and the method of Claim 4.3 for the others. Define the threshold-value θ=n𝐚(S)2g1\theta=\lceil\tfrac{n-{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}{2g}\rceil-1, call a node ZZ deep if it is a descendant of SS with d𝒯(S,Z)θd_{\cal T}(S,Z)\geq\theta, and call a vertex deep if it is stored at a deep node. A straightforward math manipulation gives the following upper bound.

Observation 4.5 ()

We have θδ2𝐚(S)+2g+1\theta\leq\delta-2\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}\rceil+2g+1.

Proof

Recall that δ=n22g+1\delta=\lfloor\tfrac{n-2}{2g}\rfloor+1 is an integer and observe that δn12g\delta\geq\tfrac{n-1}{2g}. We also know that

0(𝐚(S)2g)2=𝐚(S)4g𝐚(S)+4g20\leq(\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}-2g)^{2}={\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)-4g\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}+4g^{2}\quad
 or 𝐚(S)2g2𝐚(S)2g>2𝐚(S)2g2.\text{ or }\quad\tfrac{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}{2g}\geq 2\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}-2g>2\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}\rceil-2g-2.

Therefore

θ\displaystyle\theta =n𝐚(S)2g1n𝐚(S)12g=n12g𝐚(S)2g<δ2𝐚(S)+2g+2.\displaystyle=\lceil\tfrac{n-{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}{2g}\rceil-1\leq\tfrac{n-{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)-1}{2g}=\tfrac{n-1}{2g}-\tfrac{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}{2g}<\delta-2\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}\rceil+2g+2.

which yields the result by integrality. ∎

Claim 4.6

Assume that g3g\geq 3 and |V(S)|4g2|V(S)|\geq 4g-2 and 𝐚(S)>g2{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)>g^{2}. Then for any sV(S)s\in V(S) and any zz stored at a descendant ZZ of SS that is not deep, we have dH(s,z)δ+2g2d_{H}(s,z)\leq\delta+2g-2.

Proof

The method is exactly the same as in the proof of Claim 4.3 (walk from zz to a vertex z0V(S)z_{0}\in V(S) and apply Lemma 1 to connect z0z_{0} to ss, see also Figure 3(b)), but the analysis is different. Since ZZ is not deep, we have dH(z,z0)=d𝒯(Z,S)θ1δ2𝐚(S)+2gd_{H}(z,z_{0})=d_{\cal T}(Z,S)\leq\theta{-}1\leq\delta{-}2\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}\rceil{+}2g. By 𝐚(S)>g29{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)>g^{2}\geq 9 we have dH(z0,s)2𝐚(S)2d_{H}(z_{0},s)\leq 2\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}\rceil{-}2 and so dH(z,s)dH(z,z0)+d(z0,s)δ+2g2d_{H}(z,s)\leq d_{H}(z,z_{0})+d(z_{0},s)\leq\delta+2g-2. ∎

For deep nodes we show that there exists a suitable node with which to apply Claim 4.4. This is proved via a counting-argument: due to the (carefully chosen) threshold θ\theta otherwise more than nn vertices would be stored in tree 𝒯{\cal T}. color=blue!50!white]proof moved to the appendix

Claim 4.7

Assume that at least 4g|V(S)|+14g-|V(S)|+1 vertices are deep. Then there exists a descendant DD of SS (possibly SS itself) such that DD is an ancestor of all deep nodes, and |V(D)|2g1|V(D)|\leq 2g-1.

Proof

Let XX be the least common ancestor of all deep nodes. This is a descendant of SS as well (possibly X=SX=S), so enumerate the path from SS to XX as S=D0,D1,,Dk=XS{=}D_{0},D_{1},\dots,\allowbreak D_{k}{=}X for some k0k\geq 0. We are done if |V(Di)|2g1|V(D_{i})|\leq 2g-1 for some 0ik0\leq i\leq k, so assume (for contradiction) that the kk nodes D1,,DkD_{1},\dots,D_{k} store at least 2g2g vertices each. If XX were deep (so kθk\geq\theta) then D1,,DkD_{1},\dots,D_{k} would store at least θ2gn𝐚(S)2g\theta\cdot 2g\geq n-{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)-2g vertices. We also store 𝐚(S){\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S) vertices at strict ancestors at SS, |V(S)||V(S)| vertices at SS and at least 4g|V(S)|+14g-|V(S)|+1 deep vertices, in total hence more than nn, impossible.

So XX is not deep, see also Figure 3(d). Since XX is the least common ancestor of deep descendants, therefore it must have at least two children X1,X2X^{1},X^{2} that are ancestors of deep descendants. For j=1,2j=1,2, enumerate the path from XjX^{j} to a deep descendant of XjX^{j} as Dk+1j,,DθjD^{j}_{k+1},\dots,D^{j}_{\theta}. Then for i=k+1,,θ1i=k{+}1,\dots,\theta{-}1 node DijD_{i}^{j} is not a leaf and stores at least gg vertices, so |V(Di1)|+|V(Di2)|2g|V(D_{i}^{1})|+|V(D_{i}^{2})|\geq 2g. So for i=1,,θ1i=1,\dots,\theta-1 we can find 2g2g vertices stored at not-deep descendants of distance ii from SS. In total these not-deep strict descendants of SS hence store at least (θ1)2gn𝐚(S)4g(\theta{-}1)2g\geq n-{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)-4g vertices. As above therefore more than nn vertices are stored in the tree of peels, impossible. ∎

Now we put everything together.

Theorem 4.8

Let GG be a planar graph with n3n\geq 3 vertices and let g3g\geq 3 be an integer that is at most the fence-girth of GG. Then GG has a planar supergraph HH with rad(H)n22g+2g1\text{\rm rad}(H)\leq\lfloor\tfrac{n-2}{2g}\rfloor+2g-1. Furthermore, a vertex of HH with this eccentricity can be found in linear time.

Proof

Compute layers L0,L1,L_{0},L_{1},\dots, augmentation HH, and tree of peels 𝒯{\cal T} as in Section 3. Find separator-node SS and compute |V(S)||V(S)|, 𝐚(S),θ{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S),\theta, and the number of deep vertices. If |V(S)|4g2|V(S)|\geq 4g{-}2 and there are at least 4g|V(S)|+14g{-}|V(S)|{+}1 deep vertices, then arbitrarily fix a deep node ZZ and find node DD of Claim 4.7 by walking from SS towards ZZ until we encounter a node that stores at most 2g12g-1 vertices. In all other cases set D:=SD:=S. Pick ss as in Claim 4.4 applied to DD. Each of these steps takes linear time (we elaborate on this in Section 4.1).

Applying various cases we show that dH(s,z)δ+2g2d_{H}(s,z)\leq\delta+2g-2 for all zz (which implies the result by δ=n22g+1\delta=\lfloor\tfrac{n-2}{2g}\rfloor+1). This holds for all zV(𝒯R)z\in V({\cal T}_{R}) by Claim 4.2, so consider a vertex zz stored at a descendant ZZ of SS. The bound holds by Claim 4.3 if 𝐚(S)g2{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)\leq g^{2} and by Corollary 1 if |V(S)|4g3|V(S)|\leq 4g-3, so assume neither. If ZZ is not deep, then apply Claim 4.6. If ZZ is deep and there are at least 4g|V(S)|+14g-|V(S)|+1 deep vertices, then combine Claim 4.7 with Claim 4.4 to get the bound. The only remaining case is that 𝐚(S)>g2{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)>g^{2}, |V(S)|4g2|V(S)|\geq 4g-2, ZZ is deep, and at most 4g|V(S)|4g-|V(S)| vertices are deep (so |V(S)|4g1|V(S)|\leq 4g-1 since there is a deep vertex at ZZ). In this case, d𝒯(Z,S)=θd_{\cal T}(Z,S)=\theta, for otherwise ZZ’s parent would also be deep and store g34g|V(S)|+1g\geq 3\geq 4g-|V(S)|+1 deep vertices. Also θδ2𝐚(S)+2g+1δ1\theta\leq\delta-2\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}\rceil+2g+1\leq\delta-1 by 𝐚(S)g+1\lceil\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}\rceil\geq g+1. We used D=SD=S and picked sV(S)s\in V(S) as in Claim 4.4, so this gives dH(z,s)dT(Z,S)+V(S)2δ1+2g1d_{H}(z,s)\leq d_{T}(Z,S)+\lceil\tfrac{V(S)}{2}\rceil\leq\delta-1+2g-1 as desired. ∎

Theorem 4.8 implies C2 from the introduction, for if GG is triangulated then necessarily H=GH=G and so the radius-bound holds for the input-graph GG as well. It also implies C1:

Corollary 2

Let GG be a spherically-embedded graph with n3n\geq 3 vertices and let g3g\geq 3 be an integer that is at most the fence-girth of GG. Then GG has fse-outerplanarity at most n22g+2g\lfloor\tfrac{n-2}{2g}\rfloor+2g. Furthermore, an outerface of GG that achieves this outerplanarity can be found in linear time.

Proof

Without changing the spherical embedding of GG, compute the super-graph HH with rad(H)n22g+2g1\text{\rm rad}(H)\leq\lfloor\tfrac{n-2}{2g}\rfloor+2g-1; the outerplanarity bound holds by Obs. 1.1 and the outerface can be found by picking any face incident to the vertex ss that achieves this eccentricity. ∎

Discussion:

Theorem 4.8 requires g3g\geq 3. We can always choose such a gg if GG is simple, but if GG has parallel edges or loops (which we did not exclude) then in the fixed spherical embedding the fence-girth may only be 2 or 1. We hence briefly discuss the case g{1,2}g\in\{1,2\}. Going through all proofs where g3g\geq 3 or |V(N)|3|V(N)|\geq 3 is actually used (Lemma 1, Claim 4.2 and 4.3), one sees that the results hold for g=2g=2 if we increase the permitted distance-bound by 2. (Likewise we need to raise the permitted distance-bound in Claim 4.6 since it uses Lemma 1.) Therefore rad(H)δ+2g=n22g+1\text{\rm rad}(H)\leq\delta+2g=\lfloor\tfrac{n{-}2}{2g}\rfloor+1 for g=2g=2. For g=1g=1, rad(H)n2=n22g+1\text{\rm rad}(H)\leq\lfloor\tfrac{n}{2}\rfloor=\lfloor\tfrac{n-2}{2g}\rfloor+1 by Obs. 2.2. color=green]this paragraph could go if we need space

Theorem 4.8 also assumes that we are given gg. However, the computation of 𝒯{\cal T} does not depend on gg, and the only thing we require is that all its interior nodes store at least gg vertices. So if we are not given gg, then we can compute 𝒯{\cal T}, define g=min{|V(N)|:N is an interior node}g^{*}=\min\{|V(N)|:N\text{ is an interior node}\} and use g:=min{g,n2/2}g:=\min\{g^{*},\sqrt{n-2}/2\} as parameter for Theorem 4.8.

4.1 Run-time considerations

color=blue!50!white]this entire subsection is new and not yet very polished In this section, we elaborate on why the various steps of our algorithm can be implemented in linear time. No complicated data structures are needed for this; all bounds can be obtained via careful accounting of the visited edges.

Our first step is to compute the layers L0,L1,L_{0},L_{1},\dots, given the spherical embedding and the root-vertex rr. This can simply be done with a breadth-first search as follows. Temporarily compute the radial graph, which is a bipartite with one vertex class the vertices of GG and the other vertex class with one vertex per face of the planar embedding; it has edges whenever a face is incident to a vertex. Then the layers L0,L1,L_{0},L_{1},\dots are the same as the even-indexed BFS-layers of the radial graph if we start the breadth first search at root rr. Clearly this takes linear time to compute.

Next we must compute the augmentation HH. It follows directly from the proof in [4] that this can be done in linear time by scanning each face, but for completeness’ sake we repeat this proof and analyze the run-time here.

Claim 4.9

The augmentation HH can be computed in linear time.

Proof

We first paraphrase the proof from [4] to show that graph HH exists. Consider any face FF, and fix one vertex ww of FF that minimizes its layer-number (i.e., the index ii of the layer LiL_{i} containing ww). Break ties among choices for ww arbitrarily. For any vertex vwv\neq w on FF that is not in LiL_{i}, add an edge (v,w)(v,w) if it did not exist already. Clearly this maintains planarity since all new edges can be drawn inside face FF. Repeat at all faces to get the edges for graph HH. To see that this satisfies the condition, consider an arbitrary vertex vv in layer LiL_{i} for some i>0i>0. Thus vv was on the outer-face of G(L0Li1)G\setminus(L_{0}\cup\dots\cup L_{i-1}), but not on the outer-face of G(L0Li2)G\setminus(L_{0}\cup\dots\cup L_{i-2}). It follows that some face FF incident to vv had vertices in Li1L_{i-1}. So our procedure added an edge from vv to some vertex in face FF that is in layer Li1L_{i-1}.

To find the edges efficiently, we assume that every vertex stores its layer-number. For each face FF we can then walk along FF in O(deg(F))O(\deg(F)) time to find one vertex ww with the smallest layer-number. In a second walk along FF, add all edges (v,w)(v,w) of HH that fall within face FF. The only non-trivial step is to check whether (v,w)(v,w) already existed. This could be done with suitable data structures in O(1)O(1) amortized time per edge, but the simplest approach is to not check this at all; duplicate edges in HH do not hurt us since we add at most deg(F)\deg(F) edges per face and hence a linear number of edges in total. ∎

Our next step is to compute the tree of peels 𝒯{\cal T} of HH, for which the main challenge is to compute the connected components after we have deleted some layers L0,,LiL_{0},\dots,L_{i}. Assume we have kept track of all edges EiE_{i} that connect LiL_{i} to Li+1L_{i+1}. For each (y,z)Ei(y,z)\in E_{i} (say with zLi+1z\in L_{i+1}), walk along the face FF to the right of (y,z)(y,z), from zz and away from yy, until we reach another edge (y,z)Ei(y^{\prime},z^{\prime})\in E_{i} for which FF is to the left. Repeat at the face to the right of (y,z)(y^{\prime},z^{\prime}), and continue repeating until we return to edge (y,z)(y,z) at the face to its left. The visited vertices form the outer-face of one connected component of G(L0Li)G\setminus(L_{0}\cup\dots\cup L_{i}), so define a new node NN for them, set V(N)V(N) to be the visited vertices, and make NN the child of the node that stored yy. If there are edges of EiE_{i} left that have not been visited yet, then repeat at them to obtain the next node. At the end we have determined all nodes that together cover layer Li+1L_{i+1}. The run-time for this is proportional to |Li+1|+|Ei||L_{i+1}|+|E_{i}|, so linear over all layers.

The next few steps (in the proof of Theorem 4.8) are to compute a number of values, and to traverse 𝒯{\cal T} to determine all deep nodes and the number of vertices that they store; clearly this can be done in O(|𝒯|)O(|{\cal T}|) time. Likewise we can find the appropriate node DD to use in linear time, and finding ss can then be done in O(|V(D)|+|𝒯|)O(|V(D)|+|{\cal T}|) by using Observation 2.2 to find a central node sDs_{D} in DD and then following outgoing edges until we reach sSs\in S. The rest of the proof is an argument that the radius is small if we use ss as center, but we do not actually need to perform any computation here since we already found the appropriate ss. So overall the run-time for Theorem 4.8 is linear, and similarly one argues the run-time for Corollary 2.

4.2 Tightness

We now design graphs with large outerplanarity (relative to the fence-girth gg). (These graphs actually have girth gg, i.e., any cycle (not just those that are fences) has length at least gg.) We do this first for the fixed-spherical-embedding outerplanarity, where the lower bounds hold even for g{1,2}g\in\{1,2\}. Then, for g3g\geq 3 and at a slight decrease of the lower bound, we give bounds for the (unrestricted) outerplanarity. Roughly speaking, the graphs consist of nested cycles of length gg, with a single vertex or path inside the innermost / outside the outermost cycle, and (if desired) with edges added to ensure that all spherical embeddings have the same fse-outerplanarity. See Figure 5.

color=blue!50!white]FYI: defn of nested cycles kicked to appendix

Lemma 2 ()

For g1g\geq 1 there exists an infinite class 𝒢g={Ggk:k1 odd}\mathcal{G}_{g}=\{G_{g}^{k}:k\geq 1\text{ odd}\} of spherically embedded graphs of girth and fence-girth gg for which the fse-outerplanarity is at least k+32=n22g+32\tfrac{k+3}{2}=\tfrac{n-2}{2g}+\tfrac{3}{2}.

Before giving this proof, we briefly recall the definition of nested cycles. Let 𝒞=C0,C1,,Ck,Ck+1\mathcal{C}=\langle C_{0},C_{1},\dots,C_{k},C_{k+1}\rangle be a sequence of disjoint subgraphs in a plane graph GG. We call 𝒞\mathcal{C} nested cycles if for all 1ik1\leq i\leq k subgraph CiC_{i} is a cycle that contains C0,,Ci1C_{0},\dots,C_{i-1} inside and Ci+1,,Ck+1C_{i+1},\dots,C_{k+1} outside. Note that C0C_{0} and Ck+1C_{k+1} need not be cycles.

Proof

The graphs in 𝒢g\mathcal{G}_{g} consist of nested gg-cycles, with singletons as the innermost and outermost ‘cycles’. Formally, define CiC_{i} (for i=1,,ki=1,\dots,k) to be a gg-cycle, set C0C_{0} and Ck+1C_{k+1} to be singleton vertices, and arrange C0,,Ck+1C_{0},\dots,C_{k+1} as nested cycles to obtain the (disconnected) graph GkgG_{k}^{g} with nk=gk+2n_{k}=gk+2 vertices (see Figure 4(a)).

We claim that GkgG_{k}^{g} (for kk odd) has at least k+32\tfrac{k+3}{2} peels regardless of the choice FF of the outerface. To see this, let i{0,,k}i\in\{0,\dots,k\} be the index such that FF is incident to CiC_{i} and Ci+1C_{i+1}; up to symmetry we may assume ikii\geq k-i and therefore ik2=k+12i\geq\lceil\tfrac{k}{2}\rceil=\tfrac{k+1}{2}. Let L1,L2,L_{1},L_{2},\dots be the peels when FF is the outerface. Then L1L_{1} contains CiC_{i}, but no vertex of C0Ci1C_{0}\cup\dots\cup C_{i-1}, so we must have at least i+1i+1 peels (containing Ci,Ci1,,C1,C0C_{i},C_{i-1},\dots,C_{1},C_{0}). Since k=nk2gk=\tfrac{n_{k}{-}2}{g} therefore the number of peels is at least i+1k+32=nk22g+32i+1\geq\tfrac{k+3}{2}=\tfrac{n_{k}-2}{2g}+\tfrac{3}{2}. ∎

Theorem 4.10 ()

For g3g\geq 3 there exists an infinite class g={Hgk:k3 odd}\mathcal{H}_{g}=\{H_{g}^{k}:k\geq 3\text{ odd}\} of planar graphs of girth and fence-girth gg that have outerplanarity at least n22g+1+3+χ(g even)2g\tfrac{n-2}{2g}+1+\tfrac{3+\chi(\text{$g$ even})}{2g}.

Proof

For g=3g=3 the proof is very easy: Take graph Gk3G_{k}^{3} from Lemma 2 and arbitrarily triangulate it while respecting the given spherical embedding. The resulting graph Hk3H_{k}^{3} has a unique spherical embedding and requires (for kk odd) at least n22g+32=n22g+1+32g\tfrac{n-2}{2g}+\tfrac{3}{2}=\tfrac{n-2}{2g}+1+\tfrac{3}{2g} peels.

For g=4g=4 graph Hk4H_{k}^{4} also extends Gk4G_{k}^{4}, but we must be more careful in how to add edges to keep the fence-girth big. Recall that Gk4G_{k}^{4} consists of singleton C0C_{0}, 4-cycles C1,,CkC_{1},\dots,C_{k}, and singleton Ck+1C_{k+1}, arranged as nested cycles. Enumerate each CiC_{i} as ui,vi,wi,xi\langle u_{i},v_{i},w_{i},x_{i}\rangle, where for i=0,k+1i=0,k{+}1 all four names refer to the same vertex. Let Hk4H_{k}^{4} be the graph obtained from Gk4G_{k}^{4} by adding connector-edges (ui,vi+1)(u_{i},v_{i+1}) and (wi,xi+1)(w_{i},x_{i+1}) for i=0,,ki=0,\dots,k (see Figure 4(b)). One easily verifies that Hk4H_{k}^{4} is bipartite, hence has fence-girth 44. It also is 2-connected and hence any spherical embedding can be achieved by permuting or flipping the 3-connected components at a cutting pair [13]. But any cutting pair of Hk4H_{k}^{4} has only two cut-components (hence we cannot permute), and flipping the components gives the same graph (up to renaming) since Hk4H_{k}^{4} is symmetric. Therefore all spherical embeddings of Hk4H_{k}^{4} are the same, up to renaming of vertices. Hence for odd kk graph Hk4H_{k}^{4} has at least k+32=n22g+32=n22g+1+3+12g\tfrac{k+3}{2}=\tfrac{n-2}{2g}+\tfrac{3}{2}=\tfrac{n-2}{2g}+1+\tfrac{3+1}{2g} peels in any planar embedding.

Refer to caption
(a) G34G^{4}_{3}
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(b) H34H_{3}^{4}
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(c) H38H^{8}_{3}
Figure 4: Graphs that have large outerplanarity relative to the fence-girth.

Now consider g6g\geq 6 even, and assume that Hkg2H_{k}^{g-2} has been defined already. To obtain HkgH_{k}^{g} from it, first extend path C0C_{0} by one vertex, i.e., it becomes a path with g22\tfrac{g-2}{2} vertices from u0=v0u_{0}{=}v_{0} to w0=x0w_{0}{=}x_{0}. Likewise expand path Ck+1C_{k+1} by one vertex. Finally for 1ik1\leq i\leq k, subdivide cycle CiC_{i} twice, once on the part between uiu_{i} and viv_{i} and once on the part between wiw_{i} and xix_{i}. The connector-edges remain unchanged. Construct Hkg1H_{k}^{g-1} similarly from Hkg2H_{k}^{g-2}: expand paths C0C_{0} and Ck+1C_{k+1} by one vertex, but subdivide CiC_{i} (for 1ik1\leq i\leq k) only once, on the part between uiu_{i} and viv_{i}. For both gg even and odd, graph HkgH_{k}^{g} can be obtained by subdividing edges of Hk4H_{k}^{4}, hence its outerplanarity cannot be better and is (for kk odd) at least k+32\tfrac{k+3}{2}. Since HkgH_{k}^{g} has n=kg+g2+χ(g is odd)n=kg+g-2+\chi(\text{$g$ is odd}) vertices, hence its outerplanarity is as desired. It remains to argue the girth, so fix an arbitrary simple cycle CC in HkgH_{k}^{g} and assume that it visits Ci,,CjC_{i},\dots,C_{j} for some iji\leq j and no other nested cycles. If i=ji=j then CC equals CiC_{i} and has length gg. If i<ji<j, then CC uses at least two connector-edges, and parts of CiC_{i} and CjC_{j} that connect such connector-edges; each such part has length at least (g1)/2(g{-}1)/2 and so |C|>g|C|>g in this case. So the shortest cycle has length at least gg, and this is achieved (and the cycle is a fence) at C1C_{1}. ∎

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Figure 5: Graphs G34G^{4}_{3}, H34H^{4}_{3} and H38H^{8}_{3}.

5 Outerplanarity and Diameter

With much the same techniques as for Theorem 4.8, we can also bound the outerplanarity in terms of the diameter, as long as we permit a ‘correction-term’ of O(n)O(\sqrt{n}).

Theorem 5.1

Any simple plane graph GG with n14n\geq 14 vertices has a plane supergraph HH with rad(H)12diam(G)+2n42\text{\rm rad}(H)\leq\lceil\tfrac{1}{2}\text{\rm diam}(G)\rceil+2\sqrt{n{-}4}-2.

Proof

Compute layers L0,L1,L_{0},L_{1},\dots, augmentation HH and the tree of peels 𝒯{\cal T} as in Section 3. If diam(𝒯)=1\text{\rm diam}({\cal T})=1 then 𝒯{\cal T} consists only of root RR and its unique child NN that stores all vertices of V(G)rV(G)\setminus r; in consequence GG has outerplanarity at most 21+2n412\leq 1+2\sqrt{n{-}4}-1 by n5n\geq 5. So assume that diam(𝒯)2\text{\rm diam}({\cal T})\geq 2 and let U,VU,V be two nodes of 𝒯{\cal T} with d𝒯(U,V)=diam(𝒯)d_{\cal T}(U,V)=\text{\rm diam}({\cal T}). Let SS be the node ‘halfway between them’, i.e., of distance diam(𝒯)/2\lceil\text{\rm diam}({\cal T})/2\rceil from UU along the unique path from UU to VV in 𝒯{\cal T}. One easily verifies that d𝒯(S,Z)diam(𝒯)/2d_{\cal T}(S,Z)\leq\lceil\text{\rm diam}({\cal T})/2\rceil for all nodes ZZ of 𝒯{\cal T}, otherwise ZZ would be too far away from either UU or VV since 𝒯{\cal T} is a tree. Also note that diam(𝒯)diam(H)\text{\rm diam}({\cal T})\leq\text{\rm diam}(H) by Obs. 3.1(2) and diam(H)diam(G)\text{\rm diam}(H)\leq\text{\rm diam}(G) since HH is a supergraph. Finally observe that 𝐚(S)n4{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)\leq n{-}4, for SS is an interior node by diam(𝒯)2\text{\rm diam}({\cal T})\geq 2 and stores at least three vertices by simplicity while at least one of U,VU,V is not an ancestor of SS and stores at least one vertex.

Pick sSs\in S arbitrarily. For any zV(G)z\in V(G) (say zz is stored at node ZZ), we find a path from ss to zz as follows (see also Figure 3(b) and 3(c)): Let A0A_{0} be the least common ancestor of SS and ZZ (quite possibly A0=SA_{0}=S or A0=ZA_{0}=Z). Follow directed edges from ss and zz to reach vertices s0,z0A0s_{0},z_{0}\in A_{0}; the total number of these edges is d𝒯(S,Z)12diam(𝒯)12diam(G)d_{\cal T}(S,Z)\leq\lceil\tfrac{1}{2}\text{\rm diam}({\cal T})\rceil\leq\lceil\tfrac{1}{2}\text{\rm diam}(G)\rceil. Observe that 4n44\leq\lceil\sqrt{n{-}4}\rceil and also 𝐚(A0)𝐚(S)n4\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(A_{0})}\leq\sqrt{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathbf{a}}(S)}\leq\sqrt{n{-}4}. Using Lemma 1 therefore dH(s0,z0)2n42d_{H}(s_{0},z_{0})\leq 2\lceil\sqrt{n{-}4}\rceil-2 and so dH(s,z)12diam(G)+2n42d_{H}(s,z)\leq\lceil\tfrac{1}{2}\text{\rm diam}(G)\rceil+2\sqrt{n{-}4}-2. ∎

Theorem 5.1 implies C3 from the introduction: The fse-outerplanarity of GG is at most the fse-outerplanarity of HH, which is at most rad(H)+1\text{\rm rad}(H)+1. If GG is triangulated then necessarily H=GH=G and so rad(G)12diam(G)+O(n)\text{\rm rad}(G)\leq\lfloor\tfrac{1}{2}\text{\rm diam}(G)\rfloor+O(\sqrt{n}). Theorem 5.2 implies that the ‘correction-term’ of O(n)O(\sqrt{n}) cannot be avoided for the graph in Figure 6.

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Figure 6: Graph HH (for k=2k=2) for the proof of Theorem 5.2, both in a planar drawing and embedded on the triangular prism. Connector-edges are blue/dashed, X2X_{2} is green/bold.
Theorem 5.2 ()

For every positive integer kk, there exists a triangulated graph GG with n=(3k+1)(3k+2)n=(3k{+}1)(3k{+}2) vertices that has diameter at most 3k+13k+1 and radius at least 2k=12diam(G)+Ω(n)2k=\tfrac{1}{2}\text{\rm diam}(G)+\Omega(\sqrt{n}).

Proof

Let MM be the triangular grid of sidelength 3k3k defined as follows. Each vertex of MM corresponds to a point (x,y,z)(x,y,z) in 3\mathbb{Z}^{3} that satisfies x,y,z0x,y,z\geq 0 and x+y+z=3kx+y+z=3k. Two such points are connected if and only if their Euclidean distance is 2\sqrt{2}, i.e., one of the three coordinates has changed by +1{+}1 while another has changed by 1{-}1. Let MM^{\prime} be a second copy of this grid, and add connector-edges (v,v)(v,v^{\prime}) for any vMv\in M and vMv^{\prime}\in M^{\prime} that have the same coordinates, and one of these coordinates is 0. We can visualize the resulting graph HH as lying on the triangular prism, after omitting the zz-coordinates, see also Figure 6. Graph HH has 2i=03k(i+1)=(3k+1)(3k+2)2\cdot\sum_{i=0}^{3k}(i{+}1)=(3k{+}1)(3k{+}2) vertices as desired. It is not quite triangulated; let GG be obtained from HH by inserting arbitrary diagonals into the quadrangular faces incident to the connector-edges.

Define X3kX_{3k} to be the two vertices with xx-coordinate 3k3k. For i=0,,3k1i=0,\dots,3k-1, consider the set of all vertices that have xx-coordinate ii, and note that these form a cycle XiX_{i} of length 6k2i+26k-2i+2. Using these cycles, it is very easy to lower-bound the radius. Consider an arbitrary vertex vv, say it has coordinates (x,y,z)(x,y,z). Since x+y+z=3kx+y+z=3k we may (up to renaming of coordinates) assume that xkx\leq k. Let wX3kw\in X_{3k}. Then each of the disjoint cycles Xk+1,Xk+2,,X3k1X_{k+1},X_{k+2},\dots,X_{3k-1} contains vXkv\in X_{k} on one side and wX3kw\in X_{3k} on the other. So any path from vv to ww must contain at least one vertex from each of these cycles, and dH(v,w)2kd_{H}(v,w)\geq 2k. So any vertex has eccentricity at least 2k2k, and rad(H)2k\text{\rm rad}(H)\geq 2k.

Now we upper-bound the diameter. Fix two arbitrary vertices v,vv,v^{\prime}, and assume that they have xx-coordinates ii and jj respectively; up to renaming iji\leq j. We can walk from vXiv\in X_{i} to some vertex wXjw\in X_{j} in jij-i steps, since for all <3k\ell<3k every vertex in XX_{\ell} has at least one neighbour in X+1X_{\ell+1}. So dH(v,w)jid_{H}(v,w)\leq j-i. Vertices ww and vv^{\prime} both belong to XjX_{j}, a cycle of length 6k2j+26k-2j+2, and hence dH(w,v)3kj+1d_{H}(w,v^{\prime})\leq 3k-j+1. Therefore dH(v,v)(ji)+(3kj+1)3k+1d_{H}(v,v^{\prime})\leq(j{-}i)+(3k{-}j{+}1)\leq 3k+1 and since this holds for all vertex-pairs we have diam(H)3k+1\text{\rm diam}(H)\leq 3k+1. ∎

6 Remarks

While the ‘n2g\tfrac{n}{2g}’-part of our bound in Theorem 4.8 is tight, the ‘+2g+2g’ part could use improvement. We can easily prove (with the same techniques as in Theorem 5.1) a bound of n22g+O(n)\tfrac{n-2}{2g}+O(\sqrt{n}), but does every planar graph with fence-girth gg have outerplanarity n2g+O(1)\tfrac{n}{2g}+O(1)?

Also, our linear-time algorithm carefully side-steps the question of how to compute the fence-girth (it instead uses a parameter gg for which the node-sizes of 𝒯{\cal T} are big enough). Testing whether the fence-girth is at most kk is easily done if the spherical embedding is fixed and kk is a constant, using the subgraph isomorphism algorithm by Eppstein [16]. But the fence-girth need not be constant and Eppstein’s algorithm does not work if the embedding can be changed. Algorithms to compute the girth [10] do not seem transferrable to the fence-girth. How easy is it to compute the fence-girth, both when the spherical embedding is fixed and when it can be chosen freely?

Acknowledgments

Research by TB supported by NSERC; FRN RGPIN-2020-03958. Research by DM supported by NSERC; FRN RGPIN-2018-05023.

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